Description

StdMean is a NUMSERIES-by-1 column
vector of standard errors of estimates for each element
of the mean vector Mean.

StdCovariance is a NUMSERIES-by-NUMSERIES matrix
of standard errors of estimates for each element of the covariance
matrix Covariance.

Use this routine after estimating the mean and covariance of Data with ecmnmle.
If the mean and distinct covariance elements are treated as the parameter θ in
a complete-data maximum-likelihood estimation, then as the number
of samples increases, θ attains asymptotic
normality such that

θ−E[θ]∼N(0,I−1(θ)),

where E[θ] is
the mean and I(θ) is
the Fisher information matrix.

With missing data, the Hessian H(θ) is
a good approximation for the Fisher information (which can only be
approximated when data is missing).

It is usually advisable to use the default Method since
the resultant standard errors incorporate the increased uncertainty
due to missing data. In particular, standard errors calculated with
the Hessian are generally larger than standard errors calculated with
the Fisher information matrix.